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Description
This research examines the applicability of various wastewater based epidemiological (WBE) approaches to predicting Coronavirus disease 2019 (COVID-19) incidence in four different Maine communities.
The study analyzes the predictive fit of COVID-19 cases based on a simple predictive model, a linear regression model, and a Susceptible, Exposed, Infected, and Recovered (SEIR) differential equation model.
The findings further bolster existing evidence supporting that WBE can play a vital supplementary role in COVID-19 disease surveillance and prediction. predictive models through WBE is becoming an important public health surveillance tool and leveraging it in the future could provide numerous benefits to community level understanding and response related to population health.
Faculty Advisor(s)
Dr. Andrew Pritchard
Publication Date
Spring 2-18-2024
Notes
The research analyzed three different predictive models for COVID-19 disease prevalence based on measured SARS-CoV-2 RNA levels in community wastewater treatment plants.
The researcher compared a simple model leveraging published standards, a linear-regression model, and a SEIR differential equation model to determine whether statistically significant predictability could be observed compared to reported clinical case counts.